COURSE DESCRIPTION
WHat you will learn
About this Specialization
In this specialization you will learn how to drive change in cities and communities towards sustainable, climate friendly, just, healthy and prosperous futures, and you will boost your career with new knowledge, understanding and skills for navigating urban transformations. This specialisation brings together a series of cutting-edge courses with world-leading teachers on cities, communities, sustainability, governance and innovation.
This specialization is offered by the IIIEE at Lund University and the City Futures Academy – an online learning community on urban transformations. Our flagship course, Greening the Economy: Sustainable Cities, is ranked in the Best Online Courses of All Time by Class Central. The ranking by Class Central contains 250 courses from 100 universities based on 170,000 reviews. Our specialisation builds on the success of the Greening the Economy: Sustainable Cities course.
A key approach embedded in the courses in this specialisation is the role of experimentation in urban transformations. In particular, urban living labs are highlighted as a means for catalysing change in cities and communities towards sustainable, climate friendly, just, healthy and prosperous futures. The experimentation within urban living labs offers the potential for accelerating transformations and systematic learning across urban and national contexts.
Applied Learning Project
Learners are introduced to key facts and insights about sustainable cities and communities as engines for greening the economy, then tasked with developing this understanding through readings and practice exercises that highlight the role of urban living labs in creating sustainable cities and communities. Specifically, you will learn: how to work with greening the economy through cities and communities; how to design and implement urban living labs for accelerating change in cities and communities; how to build resilience and create a host of benefits from nature-based solutions in cities and communities; and how to influence consumption patterns in cities and communities through sharing practices . Further documentaries and quizzes will provide you with critical thinking and a broader and deeper perspective that are essential to understanding and creating sustainable cities and communities.
This course provides an understanding of automating software testing using program analysis with the goal of intelligently and algorithmically creating tests. The course covers search-based test generation, combinatorial and random testing while highlighting the challenges associated with the use of automatic test generation. You will learn: Understand algorithmic test generation techniques and their use in developer testing and continuous integration. Understand how to automatically generate test cases with assertions. Have a working knowledge and experience in static and dynamic generation of tests. Have an overview knowledge in search-based testing and the use of machine learning for test generation.
This course teaches you how to build convolutional neural networks (CNN). You will learn how to design intelligent systems using deep learning for classification, annotation, and object recognition. It includes three modules: Image processing: Introduction of industrial imaging through big data and fundamentals of image processing techniques Deep learning with convolutional neural network: Overview of neural network as classifiers, introduction of convolutional neural network and Deep learning architecture. Deep learning tools: Implementation of Deep learning for Image classification and object recognition, e.g. using Keras.
This course deals with model-based testing, a class of technologies shown to be effective and efficient in assessing the quality and correctness of large software systems. Throughout the course the participants will learn how to design and use model-based testing tools, how to create realistic models and how to use these models to automate the testing process in their organisation.
The rapid development of digital technologies and advances in communications have led to gigantic amounts of data with complex structures called ‘Big data’ being produced every day at exponential growth. The aim of this course is to give the student insights in fundamental concepts of machine learning with big data as well as recent research trends in the domain. The student will learn about problems and industrial challenges through domain-based case studies. Furthermore, the student will learn to use tools to develop systems using machine-learning algorithms in big data.
The aim of this course is to provide participants with the principles behind model-driven development of software systems and the application of such a methodology in practice. Modelling is an effective solution to reduce problem complexity and, as a consequence, to enhance time-to-market and properties of the final product.
In this course you will learn state-of-the-art statistical modelling for the purpose of analysing industrial data. The course also presents the basics of relational databases and data manipulation techniques needed to prepare the data for analysis.